The Effect of IoT New Features on Security and Privacy: New Threats, Existing Solutions, and Challenges Yet to Be Solved

Wei, Zhou; Jia, Yan; Peng, Anni; Zhang, Yuqing; Liu, Peng

doi:10.1109/jiot.2018.2847733

Cited by 366 publications

(198 citation statements)

References 38 publications

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“…Next, we focus on applying deep learning to the signals with band allocation described in Section IV-A. We con-sider t 0 ∈ [0, 2,4,6,8,10]µs, N ∈ [16,32,64] and ∆f ∈ [15,20,25,30]KHz in the training and test sets. Fig.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…However, the performance did not improve in a noticeable way. • Training: The weights in each layer of the neural network are determined by minimizing the L 2 loss ( ∆f − ∆f ) 2 where ∆f is the true parameter assumed to be known at the exploiter during the training stage. We have used Tensorflow and Adam optimizer [19] with a learning rate of 0.0005 to train the model accordingly.…”

Section: A Dnn-based Exploitationmentioning

confidence: 99%

“…The explosive growth of Internet of things (IoT) and machineto-machine (M2M) applications has created many scientific and engineering challenges in cellular systems [1], [2]. This unprecedented transformation of wireless networks will not only generate a massive amount of traffic, but it will also lead to the emergence of sophisticated cyber-attacks, which will pose a serious threat to security and privacy of communications infrastructure than ever before.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

How Secure are Multicarrier Communication Systems Against Signal Exploitation Attacks?

Nooraiepour

Hamidouche

Bajwa

et al. 2018

MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM)

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In this paper, robustness of non-contiguous orthogonal frequency division multiplexing (NC-OFDM) transmissions is investigated and contrasted to OFDM transmissions for fending off signal exploitation attacks. In contrast to ODFM transmissions, NC-OFDM transmissions take place over a subset of active subcarriers to either avoid incumbent transmissions or for strategic considerations. A point-to-point communication system is considered in this paper in the presence of an adversary (exploiter) that aims to infer transmission parameters (e.g., the subset of active subcarriers and duration of the signal) using a deep neural network (DNN). This method has been proposed since the existing methods for exploitation, which are based on cyclostationary analysis, have been shown to have limited success in NC-OFDM systems. A good estimation of the transmission parameters allows the adversary to transmit spurious data and attack the legitimate receiver. Simulation results show that the DNN can infer the transmit parameters of OFDM signals with very good accuracy. However, NC-OFDM with fully random selection of active subcarriers makes it difficult for the adversary to exploit the waveform and thus for the receiver to be affected by the spurious data. Moreover, the more structured the set of active subcarriers selected by the transmitter is, the easier it is for the adversary to infer the transmission parameters and attack the receiver using a DNN.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: A Dnn-based Exploitationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

How Secure are Multicarrier Communication Systems Against Signal Exploitation Attacks?

Nooraiepour

Hamidouche

Bajwa

et al. 2018

MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM)

View full text Add to dashboard Cite

show abstract

“…For example, if a GPS sensor is aware of the traffic situation in a different road from the user's home to work and the user's health condition (asthma) is known, then the GPS should select the route from the user's home to work that is most suitable for his health condition (less traffic and air pollution) based on the health information and traffic and pollution sensors. Similarly, [102] provided another example where a sensor senses that the indoor temperature is raised and a smart plug senses that the air cooler is turned off; then, the windows automatically open. Such interdependent processes are common in applications that utilise IoT devices to achieve a fully automated process.…”

Section: A) Threats Caused By Iot Interdependent Environmentmentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

780

333

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The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT technologies play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT ecosystem effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. The ability to monitor IoT devices intelligently provides a significant solution to new or zero-day attacks. ML/DL are powerful methods of data exploration for learning about 'normal' and 'abnormal' behaviour according to how IoT components and devices perform within the IoT environment. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems.IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

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“…We present a summary of SPS issues in emerging technologies such as IoT as discussed in [6], which in particular, comprises of an exhaustive compilation of potential security and privacy threats and challenges. Another comprehensive study about this topic is presented in [7].…”

Section: B Security Privacy and Safetymentioning

confidence: 99%

Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications

Gulhane

Vyas

Mitra

et al. 2019

2019 16th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a threedimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes attack trees to calculate a risk score for varied VRLE threats with rate and duration of threats as inputs. We compare the impact of a well-constructed attack tree with an adhoc attack tree to study the trade-offs between overheads in managing attack trees, and the cost of risk mitigation when vulnerabilities are identified. We use a vSocial VRLE testbed in a case study to showcase the effectiveness of our framework and demonstrate how a suitable attack tree formalism can result in a more safer, privacy-preserving and secure VRLE system.

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The Effect of IoT New Features on Security and Privacy: New Threats, Existing Solutions, and Challenges Yet to Be Solved

Cited by 366 publications

References 38 publications

How Secure are Multicarrier Communication Systems Against Signal Exploitation Attacks?

How Secure are Multicarrier Communication Systems Against Signal Exploitation Attacks?

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications

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